System and method of railroad track data aggregation and analysis for determining inspection frequency

ABSTRACT

A system and method for minimizing safety hazards along a railroad track by optimizing visual railroad inspection frequency based on acquired data relating to railroad safety maintenance. The system and method can provide various rating systems for the railroad assets to optimize railroad inspections by focusing the inspections to railroad assets experiencing suboptimal conditions. The ratings of the railroad assets are based on various data collected from physical factors impacting the life of the railroad assets, such as an amount of rainfall, locomotive weight and frequency of passing, and manually detected defects. The system and method can focus the railroad inspection efforts to the railroad assets requiring increased attention in response to the suboptimal conditions. The focused inspections can result in longer lifespan of the railroad assets, efficient inspections, and safer railroads.

TECHNICAL FIELD

The present disclosure relates generally to the management of railroadasset inspections, particularly systems and methods for railroad trackdata aggregation and analysis for determining inspection frequency byoptimizing visual railroad inspection frequency based on acquiredrailroad track-related data.

BACKGROUND

Manual inspection of railroad assets can be a time consuming andinefficient process. Railroad tracks have dozens of components requiringinspection and inspectors have limited time. A given railroad in theUnited States might have 10,000 miles or more of railroad track acrossmultiple states and various terrains. With such expansive work productsacross the United States, the inspectors might miss hazardous assets,which risk locomotive delays, increased wear on the railroad asset, orderailment.

Current methods of railroad asset inspections are based heavily on arecurrent method of assigning a particular inspection frequency to therailroad assets. The current methods apply a best-guess method forinspection frequency to all the railroad assets regardless of any uniqueconditions of the railroad asset or the environment. A proper inspectioncan require the close examination and documentation of many componentsand conditions. And if one component or condition is missed ormischaracterized, safety can become an issue.

The current methods overgeneralize the inspection process by applyingthe same process to each type of railroad asset. The reality of theinspection process is that good track might require fewer inspectionsthan track in suboptimal conditions. The track in suboptimal conditionscould be exposed to more extreme rainfall, more locomotives passingover, or be prone to fouling of the ballast. A railroad asset inspectionmethod could benefit from a more focused process to pinpoint where therailroad track requires maintenance based on data of the conditionsaffecting the railroad track.

SUMMARY

The present disclosure achieves technical advantages as a system andmethod for minimizing safety hazards along a railroad track byoptimizing visual railroad inspection frequency based on acquired datarelating to railroad safety maintenance. The system and method canprovide various rating systems for the particular railroad assets tooptimize railroad inspections by focusing the inspections to railroadassets experiencing suboptimal conditions. The ratings of the railroadassets are based on various data collected from physical factorsimpacting the life of the railroad assets, such as an amount ofrainfall, locomotive weight and frequency of passing, and manuallydetected defects. The system and method can focus the railroadinspection efforts to the railroad assets requiring increased attentionin response to the suboptimal conditions. The focused inspections canresult in longer lifespan of the railroad assets, efficient inspections,and safer railroads.

Accordingly, the present disclosure discloses concepts inextricably tiedto computer technology such that the present disclosure provides thetechnological benefit of optimizing railroad asset inspections throughdata analysis techniques using acquired data from multiple sources ofphysical data and a network architecture optimum for accessing andretrieving data from databases. The system can provide the inspectorswith a system configured to generate ratings of the railroad assets,focus inspection efforts, and prolong railroad asset lifespans toenhance safety. The present disclosure goes beyond mere manualinspection of the railroad assets generally assigning a frequency forrailroad asset inspection, incorporating data analysis techniques toproactively identify and anticipate railroad asset failure based onmeasured physical factors. The system can generate inspectionfrequencies for the railroad assets to optimize efficiency of theinspections, avoiding the typical generalized inspection methods orpredictive approaches. This also provides the benefit of providing anaccurate understanding of the current states of the railway assetsimplifying the inspection process by providing relevant informationneeded for a particular railroad asset, so the inspector can inspect theassets quickly, clearly, and efficiently, without inefficientduplicative work.

The present disclosure provides a technological solution missing fromconventional systems by at least aggregating disparate data andanalyzing the disparate data to generate a rating system based on dataanalysis techniques of measured physical factors impacting the railwayto proactively assign an inspection frequency to a railroad asset.Moreover, the present disclosure incorporates various factors togenerate the tiered rating for the inspection frequency. Generating theinspection frequency can be based on physical environmentalcharacteristics of the railroad asset, the frequency and weight oflocomotives passing overtop the asset, and ballast characteristics. Thetraditional approach lacks any implementation of measured physicalfactors affecting the railroad asset to assess an inspection frequency.The traditional systems simply rely on mathematical modeling ofpredicted defect formation or manual inspection of all assets, which canresult in inefficient inspector effort potentially including errorsresulting in a hazardous environments. In this manner, the traditionalapproach is lacking a subjective approach to railroad asset inspection.The present disclosure avoids adding strain on an already overspentsystem. The system and methods of the present disclosure can alsoprovide at least the following functionality:

-   -   Generating tiered ratings for railroad asset inspection        frequencies based on measured data of physical factors;    -   Providing an efficient railroad asset inspection method for        enhanced railroad safety;    -   Generating an analysis of the railroad asset based on measured        characteristics of the railroad asset; and    -   Providing a method compliant with relevant regulations for        railroad travel and safety.

Further, a visual inspection model solves the problem of combiningseveral factors for assessing track inspection requirements such that itrefreshes and updates with data for inspectors to use to inspect trackat a desired frequency. A system according to the present disclosure canreceive data from a geometry car, track predetermined features, trafficcharacteristics, manual defects, and combine them over individual tracksegments to generate a value for assessing frequency of visual trackinspections. Variables can account for track conditions based on valuesand inputs. The variables can be leading indicators for concerns withtrack conditions. A Visual Inspection Model can change with tracksegment conditions and can be adaptive to received data, such that it is“data driven.” Segment escalation for increase visual inspections iftesting invalid or not tested. The system can treat all input variablesas categorical quantities, such as CSP, GeoTag, and ClassDrop inputs.

Advantageously, the system transforms the received inputs, such as BFI,MGT, and Rainfall by converting the inputs from continuous variables towhole-integer values and rescaling (e.g., normalizing) the values tomatch the categorical variables before contributing to the total score.An IF Model can be a scoring system that assigns specific section oftrack to one of three inspection protocols based on associated valuesfor MGT, BFI, Rainfall, ClassDrop, CSP Category, Manual Defect, and CWJoint Count. The calculated score (which can be a function of theindividual score values calculated for each of the input variables) canbe reclassed to one of the three inspection protocols. For the purposeof assessing model sensitivity to each of these input variables, thescore can be calculated as a model result, rather than the associatedinspection protocol. The IF Model can generate a single score for eachinput track. In a “full-factorial” design, every possible combination ofinput value would be used to generate a series of model results, and theresult values can be plotted as functions of various inputs.

The general process can produce a fixed number of track records, holdingone input variable at a fixed value, and allowing all others to be drawnfrom a random pool of values based on the empirical density function forthat variable. Those records can be written to a text data file with astructure that resembles each of the weekly model reports. Those datafiles can then be used as input to an application that calculatesvarious reporting metrics. The system can duplicate the calculationsteps derived from weekly track inspection category reports. The systemcan also generate various statistics based on the weekly data recordsand can generate pseudo-random data records to satisfy the criteriaassociated with specific analytic scenarios, as well as the metrics usedto assess model behavior.

It is an object of the invention to provide a system for minimizingsafety hazards along a railroad track by optimizing visual railroadinspection frequency based on acquired data relating to railroad safetymaintenance. It is a further object of the invention to provide a methodfor minimizing safety hazards along a railroad track by optimizingvisual railroad inspection frequency based on acquired data relating torailroad safety maintenance. It is a further object of the invention toprovide a computer-implemented method for minimizing safety hazardsalong a railroad track by optimizing visual railroad inspectionfrequency based on acquired data relating to railroad safetymaintenance. These and other objects are provided by at least thefollowing embodiments.

In one embodiment, a system for minimizing safety hazards along arailroad track by optimizing visual railroad inspection frequency basedon acquired data relating to railroad safety maintenance, comprising: amemory for storing the acquired data relating to the railroad safetymaintenance and an inspection frequency classification model totransform the acquired data from a raw form to a final inspectionfrequency value; and a processor that is configured to transform theacquired data relating to the railroad safety maintenance from the rawform to the final inspection frequency value, by performing the stepsof: receiving the acquired data relating to railroad safety maintenancefor at least one segment of railroad track; assigning a railroad safetyvalue for each of a plurality of categories of the acquired data basedon a user defined hazard model; aggregating the railroad safety valuesto generate a railroad safety score; when a metric gross ton (MGT)calculation includes a rail joint factor, multiplying the railroadsafety score by a user defined multiplier; and generating the finalinspection frequency value for the at least one segment of railroadtrack based on the railroad safety score. Wherein the processor isfurther configured to perform the step of segmenting the acquired datainto the plurality of categories. Wherein the plurality of categoriesincludes comprehensive service plan (CSP), MGT, precipitation, geometrycar tag, ballast fouling index (BFI), slow order, and manual defects.Wherein the geometry car tag includes an open tag or a closed tag.Wherein the open tag and the closed tag each correspond to a first tagtype, a second tag type, or a third tag type. Wherein the processor isfurther configured to perform the step of determining whether toincrease an inspection frequency of the segment of railroad track whenthe segment of railroad track corresponds to a minimum inspectioncriteria threshold. Wherein the minimum inspection criteria thresholdincludes a measurement of at least 12 MGT across the segment of railroadtrack since a previous test. Wherein the inspection frequency increasesto a next available railroad manual inspection frequency. Wherein thefinal inspection frequency value is based on user defined inspectionthresholds including a first railroad manual inspection frequency, asecond railroad manual inspection frequency, and a third railroad manualinspection frequency. Wherein the first railroad manual inspectionfrequency is two inspections per month, the second railroad manualinspection frequency is one inspection per week, and the third railroadmanual inspection frequency is three inspections per week.

In another embodiment, a method for minimizing safety hazards along arailroad track by optimizing visual railroad inspection frequency basedon acquired data relating to railroad safety maintenance, comprising:receiving the acquired data relating to railroad safety maintenance forat least one segment of railroad track; assigning a railroad safetyvalue for each of a plurality of categories based on a user definedhazard model; aggregating the railroad safety values to generate arailroad safety score; when a metric gross ton (MGT) calculationincludes a rail joint factor, multiplying the railroad safety score by auser defined multiplier; and generating the final inspection frequencyvalue for the at least one segment of railroad track based on therailroad safety score. Wherein the processor is further configured toperform the step of segmenting the acquired data into the plurality ofcategories. Wherein the plurality of categories includes comprehensiveservice plan (CSP), MGT, precipitation, geometry car tag, ballastfouling index (BFI), slow order, and manual defects. Wherein thegeometry car tag includes an open tag or a closed tag. Wherein the opentag and the closed tag each correspond to a first tag type, a second tagtype, or a third tag type. Wherein the processor is further configuredto perform the step of determining whether to increase an inspectionfrequency of the segment of railroad track when the segment of railroadtrack corresponds to a minimum inspection criteria threshold. Whereinthe minimum inspection criteria threshold includes a measurement of atleast 12 MGT across the segment of railroad track since a previous test.Wherein the inspection frequency increases to a next available railroadmanual inspection frequency. Wherein the final inspection frequencyvalue is based on user defined inspection thresholds including a firstrailroad manual inspection frequency, a second railroad manualinspection frequency, and a third railroad manual inspection frequency.Wherein the first railroad manual inspection frequency is twoinspections per month, the second railroad manual inspection frequencyis one inspection per week, and the third railroad manual inspectionfrequency is three inspections per week.

In another embodiment, a computer-implemented method for minimizingsafety hazards along a railroad track by optimizing visual railroadinspection frequency based on acquired data relating to railroad safetymaintenance, the computer-implemented method comprising: receiving theacquired data relating to railroad safety maintenance for at least onesegment of railroad track; assigning a railroad safety value for each ofa plurality of categories based on a user defined hazard model;aggregating the railroad safety values to generate a railroad safetyscore; when a metric gross ton (MGT) calculation includes a rail jointfactor, multiplying the railroad safety score by a user definedmultiplier; and generating the final inspection frequency value for theat least one segment of railroad track based on the railroad safetyscore. Wherein the processor is further configured to perform the stepof segmenting the acquired data into the plurality of categories.Wherein the plurality of categories includes comprehensive service plan(CSP), MGT, precipitation, geometry car tag, ballast fouling index(BFI), slow order, and manual defects. Wherein the geometry car tagincludes an open tag or a closed tag. Wherein the open tag and theclosed tag each correspond to a first tag type, a second tag type, or athird tag type. Wherein the processor is further configured to performthe step of determining whether to increase an inspection frequency ofthe segment of railroad track when the segment of railroad trackcorresponds to a minimum inspection criteria threshold. Wherein theminimum inspection criteria threshold includes a measurement of at least12 MGT across the segment of railroad track since a previous test.Wherein the inspection frequency increases to a next available railroadmanual inspection frequency. Wherein the final inspection frequencyvalue is based on user defined inspection thresholds including a firstrailroad manual inspection frequency, a second railroad manualinspection frequency, and a third railroad manual inspection frequency.Wherein the first railroad manual inspection frequency is twoinspections per month, the second railroad manual inspection frequencyis one inspection per week, and the third railroad manual inspectionfrequency is three inspections per week.

In another embodiment, a method for assessing frequency of visual trackinspections, can include: receiving data related to a railroad tracksegment; generating continuous variables and categorical variablesrepresenting track segment condition indicators of concern; convertingand rescale continuous variables to match categorical variables;generating an inspection value for each of the variables; and generatinga model that assigns a specific section of track to one of a pluralityof inspection protocols based on the inspection values. Furthercomprising generating a series of model results for every combination ofinput value. Further comprising holding a first variable at a fixedvalue and allowing all other variables to be drawn from a random pool ofvalues based on the empirical density function for that variable.Wherein the model can escalate a track segment for increased visualinspections if testing invalid or not tested. Wherein the inspectionprotocols can be three times a week, once a week, or twice per month.Wherein the inspection protocols can be based on associated values forMGT, BFI, Rainfall, ClassDrop, CSP Category, Manual Defect, and CW JointCount.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be readily understood by the followingdetailed description, taken in conjunction with the accompanyingdrawings that illustrate, by way of example, the principles of thepresent disclosure. The drawings illustrate the design and utility ofone or more exemplary embodiments of the present disclosure, in whichlike elements are referred to by like reference numbers or symbols. Theobjects and elements in the drawings are not necessarily drawn to scale,proportion, or precise positional relationship. Instead, emphasis isfocused on illustrating the principles of the present disclosure.

FIGS. 1A and 1B illustrate a data flow diagram of a railroad assetinspection system, in accordance with one or more exemplary embodimentsof the present disclosure;

FIGS. 2A and 2B illustrate a flowchart of a process for railroad assetinspection, in accordance with one or more exemplary embodiments of thepresent disclosure;

FIG. 3 illustrates a schematic view of a railroad asset maintenancesystem, in accordance with one or more exemplary embodiments of thepresent disclosure;

FIG. 4 illustrates a schematic view of a railroad inspection system, inaccordance with one or more exemplary embodiments of the presentdisclosure;

FIG. 5 illustrates railroad inspection control logic, in accordance withone or more exemplary embodiments of the present disclosure;

FIGS. 6A-6C illustrate exemplary diagrams for a railroad inspectionsystem, in accordance with one or more exemplary embodiments of thepresent disclosure; and

FIG. 7 illustrates a process for railroad inspection, in accordance withone or more exemplary embodiments of the present disclosure.

DETAILED DESCRIPTION

The disclosure presented in the following written description and thevarious features and advantageous details thereof, are explained morefully with reference to the non-limiting examples included in theaccompanying drawings and as detailed in the description. Descriptionsof well-known components have been omitted to not unnecessarily obscurethe principal features described herein. The examples used in thefollowing description are intended to facilitate an understanding of theways in which the disclosure can be implemented and practiced. A personof ordinary skill in the art would read this disclosure to mean that anysuitable combination of the functionality or exemplary embodiments belowcould be combined to achieve the subject matter claimed. The disclosureincludes either a representative number of species falling within thescope of the genus or structural features common to the members of thegenus so that one of ordinary skill in the art can recognize the membersof the genus. Accordingly, these examples should not be construed aslimiting the scope of the claims.

A person of ordinary skill in the art would understand that any systemclaims presented herein encompass all of the elements and limitationsdisclosed therein, and as such, require that each system claim be viewedas a whole. Any reasonably foreseeable items functionally related to theclaims are also relevant. Pursuant to MPEP § 904, the Examiner, afterhaving obtained a thorough understanding of the invention disclosed andclaimed in the nonprovisional application has searched the prior art asdisclosed in patents and other published documents, i.e., nonpatentliterature. Therefore, as evidenced by the issuance of this patent, theprior art fails to disclose or teach the elements and limitationspresented in the claims as enabled by the specification and drawings,such that the presented claims are patentable under 35 U.S.C. §§ 101,102, 103, and 112.

FIGS. 1A and 1B illustrate a data flow diagram of a railroad assetinspection system 100, in accordance with one or more exemplaryembodiments of the present disclosure. The railroad asset inspectionsystem 100 can include comprehensive service plan (CSP) data 102,million gross tons (MGT) data 104, precipitation data 106, geometry tagdata 108, ballast fouling index (BFI) data 110, slow order data 112,manual defect data 114, a total score 116, a rail joint decision point118, a multiplier 120, a final rating 122, a final rating transformation124, a temporary inspection frequency 126, an MGT decision point 128, ascheduling decision point 130, an elevated inspection frequency 132, aFederal Railroad Administration (FRA) minimum inspection frequency 134,and a final inspection frequency 136.

The aforementioned system steps can be instantiated in hardware,software, or a suitable combination of hardware and software therefor,and may comprise one or more software systems operating on one or moreservers, having one or more processors, with access to memory. Theservers can include electronic storage, one or more processors, and/orother components. The servers can include communication lines,connections, and/or ports to enable the exchange of information via anetwork and/or other computing platforms. The servers can also include aplurality of hardware, software, and/or firmware components operatingtogether to provide the functionality attributed herein to the servers.For example, the servers can be implemented by a cloud of computingplatforms operating together as a server, including SaaS, IaaS, and PaaSfunctionality. Additionally, the servers can include the memory.

The structural components of the steps can be communicably coupled toeach other via a network (e.g., network 340 in FIG. 3 ), such that datacan be transmitted. The network can be the Internet, intranet, or othersuitable network. The data transmission can be encrypted, unencrypted,over a VPN tunnel, or other suitable communication means. The networkcan be a WAN, LAN, PAN, or other suitable network type. The networkcommunication between the system components can be encrypted using PGP,Blowfish, Twofish, 3DES, HTTPS, or other suitable encryption. The system100 can be configured to provide communication via the various systems,components, and modules disclosed herein via an API, PCI, PCI-Express,ANSI-X12, Ethernet, Wi-Fi, Bluetooth, or other suitable communicationprotocol or medium. Additionally, third party systems and databases canbe operably coupled to the system components via the network.

The data transmitted to and from the components of system 100, caninclude any format, including JSON, TCP/IP, XML, HTML, ASCII, SMS, CSV,REST, or other suitable format. The data transmission can include amessage, flag, header, header properties, metadata, and/or a body, or beencapsulated and packetized by any suitable format having same.

The CSP data 102, in an embodiment, can include information about theCSP. The CSP information can include, in addition to work ordersthemselves, data from a geometry car used to segment the tracksequentially into non-CSP (track with no measured values out ofcompliance) and CSP (track with at least one measured value out ofcompliance) sections. The track sections bounded at these CSP/non-CSPtransitions compose the individual records for which inspectionfrequency classifications are calculated. CSP sections of trackincrement a CSP data value by 1, 2, or 3 depending on the degree towhich the measured values deviate from the acceptable range.

The MGT data 104, in an embodiment, can include information about theMGT across the segment of track. The MGT information can include datacorresponding to specific requirements for certain classes of track setby the FRA. Not only is the MGT aggregated during the previous calendaryear a decision criterion, but the maximum allowed aggregated MGTbetween inspections is also regulated for certain track. Depending onthe specific kind of track involved, MGT categories used by the FRA forvarious regulations are bounded by these values: 2, 10, 20, 40, and 60.In an example, system 100 can recode the MGT calculated for each tracksection according to this table.

MGT Coded score   <40 1 40-80 2   >80 3The MGT values for the pilot track are always non-zero, ranging from0.018 to 191.942.

The precipitation data 106, in an embodiment, can include informationabout the precipitation across the segment of track. The precipitationinformation can include data corresponding to wet precipitationaggregated during the previous week is tabulated. Frost, ice, aggregatedsnow, and snowmelt are not included in the IF Model. The actual data areprovided as part of an AccuWeather subscription service.

Rainfall Coded score   0” 0 (or trace amount) 0”-1” 1 1”-2” 2   >2” 3

The geometry tag data 108, in an embodiment, can include informationabout the defects corresponding to the segment of track. The geometrytag information can include rail gauge, deflection under load, and othermeasurements are taken by instruments carried by a special car that runsover all track on a regular basis. The results of these geometry carruns are used to generate work orders of various kinds. The work ordersare color-coded to indicate defect severity and are used directly forscoring as shown below.

Yellow Tag Orange Tag Red Tag Coded score 0 0 0 0  1+ N/A N/A 1 N/A  1+N/A 2 N/A N/A  1+ 3

The BFI data 110, in an embodiment, can include information about theballast for segment of track. The BFI information can include the amountof fine particulate matter lodged within the gaps between larger rockand gravel composing the ballast, or railbed, is associated with loss ofthe ability of the ballast to support load without shifting to adangerous degree. An index of the degree to which ballast has been“fouled” is provided by railcars with ground-penetrating radarinstrumentation. Observed values range from near zero to 200. Track istypically scanned twice a year, in 15′ sections. As a rule of thumb,track inspectors would typically flag sections of track having a BFI of55 or above as defective based on visual cues alone.

BFI Coded score   <20 1 20-29 2 30-39 3   >40 4

The slow order data 112, in an embodiment, can include information abouta speed restriction across the segment of track. The slow orderinformation can include a reduction in maximum allowed speed for freightand/or passenger train runs is an administrative action which oftenindicates that a defect of some kind has been observed or modificationsto a track are in progress or have taken place. Almost all speedreductions will result in a reduction in the FRA Class rating for theaffected track. The change in freight track Class number is useddirectly for scoring as follows.

Class Drop Coded score No Slow Order 0 No Change in Class 1 Class Dropby 1 2 Class Drop by 2 3

The manual defect data 114, in an embodiment, can include informationabout defects identified manually. The manual defect information caninclude when rail and/or railbed defects are observed while performingother tasks, inspectors and staff can record the specific defect in awork order database. For a defined group of defects, the InspectionFrequency Score is automatically raised to the maximum value (5).

The total score 116, in an embodiment, can include an aggregated valuecorresponding to the above-described data values. The total score 116can have a maximum of 48.5.

The rail joint decision point 118, in an embodiment, can includeinformation corresponding to whether the segment of track includes arail joint. The rail joint information can include data corresponding towhether the load-bearing strength of individual sections of rail isreduced at points where the rail is interrupted, particularly at placeswhere repairs to the rail or bed have taken place. It is common forthese temporary joints to persist for a short time after repairactivities, but normally they are replaced by welded joints. In anexample, the total score 116 can be multiplied the multiplier 120.

The multiplier 120, in an embodiment, can multiply the total score 116by 1.5 if an aggregated MGT is over a user defined threshold for a railjoint. For example, the user defined threshold can be 20 MGT.

The final rating 122, in an embodiment, can include the aggregated scorefor the above factors. For example, the above factors can add up to ascore associated with various visual inspection frequencies.

The final rating transformation 124, in an embodiment, can include aconditional transformation of the final rating 122 to a visualinspection frequency. For example, when the final rating 122 is between0 and 6, the visual inspection frequency is twice per month. In anotherexample, when the final rating 122 is between 6 and 11, the visualinspection frequency is one time per week. In another example, when thefinal rating 122 is greater than or equal to 12, the visual inspectionfrequency is three times per week.

The temporary inspection frequency 126, in an embodiment, can includethe visual inspection frequency from the final rating transformation124. For example, the temporary inspection frequency 126 can include thevisual inspection frequency as twice per month, one time per week, orthree times per week.

The MGT decision point 128, in an embodiment, can include adetermination of whether the segment of track withstood a weight on thetrack greater than a MGT threshold. For example, the MGT threshold caninclude 12 MGT. If the segment of track withstood a weight greater thanthe MGT threshold, the visual inspection frequency is elevated. If thesegment of track withstood a weight less than the MGT threshold, thesystem 100 determines whether the scheduling meets a requirementstandard.

The scheduling decision point 130, in an embodiment, can include adetermination of whether the scheduling meets a requirement standard.For example, the requirement standard can include an ATGMS requirement.If the segment of track is scheduled for greater than the requirementstandard, the visual inspection frequency is the FRA minimum inspectionfrequency 134. If the segment of track is scheduled for less than therequirement standard, the temporary inspection frequency 126 is thefinal inspection frequency 136.

The elevated inspection frequency 132, in an embodiment, can include anincrease in the visual inspection frequency. For example, the increasein the visual inspection frequency can increase to a next inspectionfrequency. In an example, the visual inspection frequency can increasefrom twice per month to one time per week. In another example, thevisual inspection frequency can increase from one time per week to threetimes per week. In another example, the visual inspection frequency canremain at three times per week.

The FRA minimum inspection frequency 134, in an embodiment, can includea minimum visual inspection frequency allowable by legal regulation. Forexample, the FRA can establish the minimum inspection frequency by lawto twice per month, one time per week, three times per week, or someother minimum visual inspection frequency.

The final inspection frequency 136, in an embodiment, can include thevisual inspection frequency an inspector will apply to the segment oftrack. For example, when the final inspection frequency 136 is twice permonth, the inspector will visit the segment of track twice per month toinspect for any hazardous situations, defects, or potential issues.

FIGS. 2A and 2B illustrates a flowchart of a process for railroad assetinspection 200, in accordance with one or more exemplary embodiments ofthe present disclosure. The process for railroad asset inspection 200can be implemented as algorithm(s) on a computer processor (e.g., vitallogic controller, onboard computer, server), a machine learning module,or other suitable system. Additionally, the process for railroad assetinspection 200 can be achieved with software, firmware, hardware, anapplication programming interface (API), a network connection, a networktransfer protocol, HTML, DHTML, JavaScript, Dojo, Ruby, Rails, othersuitable applications, or a suitable combination thereof. The processfor railroad asset inspection 200 can be implemented in a computerenvironment (e.g., physical computer processor, virtual machine) can becapable of transforming acquired data relating to the railroad safetymaintenance from a raw form to a final inspection frequency value andoperably coupled to a memory storing the acquired data relating to therailroad safety maintenance and an inspection frequency classificationmodel to transform the acquired data from a raw form to a finalinspection frequency value.

The process for railroad asset inspection 200 can leverage the abilityof a computer platform to spawn multiple processes and threads byprocessing data simultaneously. The speed and efficiency of the processfor railroad asset inspection 200 can be greatly improved byinstantiating more than one process for managing dual westbound trains.However, one skilled in the art of programming will appreciate that useof a single processing thread may also be utilized and is within thescope of the present disclosure. The process for railroad assetinspection 200 can also be distributed amongst a plurality of networkedcomputer processors. The computer processors can be located in servers(e.g., 302 in FIG. 3 ). The process for railroad asset inspection 200 ofthe present embodiment begins at step 202.

At step 202, in an embodiment, the process 200 can include locomotivestravelling along a segment of track. For example, the locomotives caninclude passenger locomotives, cargo locomotives, geometry tag cars, orany other type of locomotive traveling along the segment of track. In anexample, the locomotive is used to gather data corresponding to physicalcharacteristics of the segment of track. For example, when thelocomotive is a geometry tag car, the locomotive can identify defectsalong the segment of track and assign a tag to the defect. In anotherexample, the locomotive can carry a payload for transport across thesegment of track. The process 200 then proceeds to step 204.

At step 204, in an embodiment, the process 200 can instruct hardwarecomponents to collect CSP data using external sensors along the trackand the system 200 can receive the data and assign a CSP value to theCSP data. The process 200 can store and access the CSP data in anexternal database corresponding to a network architecture optimum fordata access and retrieval. For example, the external database cancorrespond to a permanent database for storage of all railwayinformation. Alternatively, the external database can correspond to atemporary database for temporary storage of the railway data for rapidaccess. The CSP data can include, in addition to work orders themselves,data from a geometry car used to segment the track sequentially intonon-CSP (track with no measured values out of compliance) and CSP (trackwith at least one measured value out of compliance) sections. The tracksections bounded at these CSP/non-CSP transitions compose the individualrecords for which inspection frequency classifications are calculated.The process 200 then proceeds to step 206.

At step 206, in an embodiment, the process 200 can instruct hardwarecomponents to collect MGT data using external sensors along the trackand the system 200 can receive the data and assign an MGT value to theMGT data. The process 200 can store and access the MGT data in theexternal database corresponding to the network architecture optimum fordata access and retrieval. For example, the external database cancorrespond to a permanent database for storage of all railwayinformation. Alternatively, the external database can correspond to atemporary database for temporary storage of the railway data for rapidaccess. The process 200 can collect the MGT data using external sensorscorresponding to the segment of track measuring the MGT. In anotherexample, the process 200 can collect the MGT data using a record oflocomotives passing across the segment of track. The process 200 thenproceeds to step 208.

At step 208, in an embodiment, the process 200 can instruct hardwarecomponents to collect precipitation data using external sensors alongthe track and the system 200 can receive the data and assign aprecipitation value to the precipitation data. For example, theprecipitation data can include information about the precipitationacross the segment of track. The process 200 can store and access theprecipitation data in the external database corresponding to the networkarchitecture optimum for data access and retrieval. For example, theexternal database can correspond to a permanent database for storage ofall railway information. Alternatively, the external database cancorrespond to a temporary database for temporary storage of the railwaydata for rapid access. The process 200 then proceeds to step 210.

At step 210, in an embodiment, the process 200 can instruct hardwarecomponents to collect geometry tag data using external sensorscorresponding to the segment of track and the system 200 can receive thedata and assign a geometry tag value to the geometry tag data. Thegeometry tag data can include information about the defectscorresponding to the segment of track. The process 200 can store andaccess the geometry tag data in the external database corresponding tothe network architecture optimum for data access and retrieval. Forexample, the external database can correspond to a permanent databasefor storage of all railway information. Alternatively, the externaldatabase can correspond to a temporary database for temporary storage ofthe railway data for rapid access. The process 200 then proceeds to step212.

At step 212, in an embodiment, the process 200 can instruct hardwarecomponents to collect BFI data using external sensors along the trackand the system 200 can receive the data and assign a BFI value to theBFI data. For example, the BFI data can include information about theballast for segment of track. The process 200 can store and access theBFI data in the external database corresponding to the networkarchitecture optimum for data access and retrieval. For example, theexternal database can correspond to a permanent database for storage ofall railway information. Alternatively, the external database cancorrespond to a temporary database for temporary storage of the railwaydata for rapid access. The process 200 then proceeds to step 214.

At step 214, in an embodiment, the process 200 can instruct hardwarecomponents to collect slow order data using external sensors along thetrack and the system 200 can receive the data and assign a slow ordervalue to the slow order data. The slow order data can includeinformation about a speed restriction across the segment of track. Theprocess 200 can store and access the slow order data in the externaldatabase corresponding to the network architecture optimum for dataaccess and retrieval. For example, the external database can correspondto a permanent database for storage of all railway information.Alternatively, the external database can correspond to a temporarydatabase for temporary storage of the railway data for rapid access. Theprocess 200 then proceeds to step 216.

At step 216, in an embodiment, the process 200 can instruct hardwarecomponents to collect manual defect data using external sensors alongthe track and the system 200 can receive the data and assign a manualdefect value to the manual defect data. For example, the manual defectdata can include information about defects identified manually. Theprocess 200 can store and access the slow order data in the externaldatabase corresponding to the network architecture optimum for dataaccess and retrieval. For example, the external database can correspondto a permanent database for storage of all railway information.Alternatively, the external database can correspond to a temporarydatabase for temporary storage of the railway data for rapid access. Theprocess 200 then proceeds to step 218.

At step 218, in an embodiment, the process 200 can instruct hardwarecomponents to add the above values corresponding to the railway safetydata to generate a total score. The process 200 then proceeds to step220.

At step 220, in an embodiment, the process 200 can determine whether theMGT data for the segment of track includes information indicating theexistence of a rail joint along the segment of track. For example, theprocess 200 can parse the MGT data to identify digital variablescorresponding to whether MGT data include the information correspondingto existence of the rail joint for the segment of track. In an example,the MGT data can include a variable indicating the existence of the railjoint along the segment of track using a binary value. For example, a“0” can indicate the segment of track lacks any rail joint.Alternatively, a “1” can indicate the segment of track includes at leastone rail joint. If the MGT data includes information about a rail joint,the process 200 then proceeds to step 222. If the MGT data does notinclude information about a rail joint, the process 200 then proceeds tostep 224.

At step 222, in an embodiment, the process 200 can instruct hardwarecomponents to multiply the total score by a user defined multiplier. Forexample, the user defined multiplier can be 1.5. The process 200 thenproceeds to step 224.

At step 224, in an embodiment, the process 200 can instruct hardwarecomponents to convert the total score to a corresponding inspectionfrequency. The process 200 then proceeds to step 226.

At step 226, in an embodiment, the process 200 can determine whether theMGT value is greater than an expected MGT value and the inspectionfrequency is below an expected frequency value. If the segment of trackregisters the MGT value greater than the expected MGT value and aninspection frequency lower than the expected frequency value, theprocess 200 then proceeds to step 228. If the segment of track registersthe MGT value less than the expected MGT value or an inspectionfrequency greater than the expected frequency value, the process 200then proceeds to step 230.

At step 228, in an embodiment, the process 200 can increase theinspection frequency. The process 200 then proceeds to step 230.

At step 230, in an embodiment, the process 200 can apply the inspectionfrequency for the segment of track. The process 200 then terminates orawaits a new inspection creation request and can repeat theaforementioned steps.

FIG. 3 illustrates a schematic view of a railroad asset maintenancesystem 300, in accordance with one or more exemplary embodiments of thepresent disclosure. The system 300 can include one or more servers 102having one or more processor(s) 104, a memory 130, machine-readableinstructions 106, including a CSP data collection module 308, MGT datacollection module 310, precipitation data collection module 312,geometry car tag collection module 314, BFI data collection module 316,slow order data collection module 318, manual defect collection module320, message identification module 322, log collection module 324,information parsing module 326, rating module 328, accumulation module330, analysis module 332, among other relevant modules. The server 102can be operably coupled to one or more clients via a network 140. Theclient can be a mobile communication device 150, a personal laptop 352,external sensors 354, or another type of hardware or software forcommunicating data over the network 340. In another exemplaryembodiment, the mobile communication device 150, the personal laptop352, or external sensors 354 can include an application configured tocommunicate with the server 102 over the network 140.

The aforementioned system components (e.g., server(s) 102 and clients350, 352, 354) can be communicably coupled to each other via the network140, such that data can be transmitted. The network 140 can be theInternet, intranet, or other suitable network. The data transmission canbe encrypted, unencrypted, over a VPN tunnel, or other suitablecommunication means. The network 140 can be a WAN, LAN, PAN, or othersuitable network type. The network communication between the clients,server 302, or any other system component can be encrypted using PGP,Blowfish, Twofish, AES, 3DES, HTTPS, or other suitable encryption. Thesystem 100 can be configured to provide communication via the varioussystems, components, and modules disclosed herein via an applicationprogramming interface (API), PCI, PCI-Express, ANSI-X12, Ethernet,Wi-Fi, Bluetooth, or other suitable communication protocol or medium.Additionally, third party systems and databases can be operably coupledto the system components via the network 140.

The data transmitted to and from the components of system 300 (e.g., theserver 302 and clients 350, 352, 354), can include any format, includingJavaScript Object Notation (JSON), TCP/IP, XML, HTML, ASCII, SMS, CSV,representational state transfer (REST), or other suitable format. Thedata transmission can include a message, flag, header, headerproperties, metadata, and/or a body, or be encapsulated and packetizedby any suitable format having same.

The server(s) 102 can be implemented in hardware, software, or asuitable combination of hardware and software therefor, and may compriseone or more software systems operating on one or more servers, havingone or more processor(s) 104, with access to memory 130. Server(s) 102can include electronic storage, one or more processors, and/or othercomponents. Server(s) 102 can include communication lines, connections,and/or ports to enable the exchange of information via a network (e.g.,the network 140) and/or other computing platforms. Server(s) 102 canalso include a plurality of hardware, software, and/or firmwarecomponents operating together to provide the functionality attributedherein to server(s) 102. For example, server(s) 102 can be implementedby a cloud of computing platforms operating together as server(s) 102,including Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS)functionality. Additionally, the server(s) 102 can include memory 130.

Memory 130 can comprise electronic storage that can includenon-transitory storage media that electronically stores information. Theelectronic storage media of electronic storage can include one or bothof system storage that can be provided integrally (e.g., substantiallynon-removable) with server(s) 102 and/or removable storage that can beremovably connectable to server(s) 102 via, for example, a port (e.g., aUSB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.).Electronic storage may include one or more of optically readable storagemedia (e.g., optical disks, etc.), magnetically readable storage media(e.g., magnetic tape, magnetic hard drive, floppy drive, etc.),electrical charge-based storage media (e.g., EEPROM, RAM, etc.),solid-state storage media (e.g., flash drive, etc.), and/or otherelectronically readable storage media. Electronic storage may includeone or more virtual storage resources (e.g., cloud storage, a virtualprivate network, and/or other virtual storage resources). The electronicstorage can include a database, or public or private distributed ledger(e.g., blockchain). Electronic storage can store machine-readableinstructions 106, software algorithms, control logic, data generated byprocessor(s), data received from server(s), data received from computingplatform(s), and/or other data that can enable server(s) to function asdescribed herein. The electronic storage can also include third-partydatabases accessible via the network 140.

Processor(s) 104 can be configured to provide data processingcapabilities in server(s) 102. As such, processor(s) 104 can include oneor more of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information, such as FPGAs or ASICs. The processor(s) 104 canbe a single entity or include a plurality of processing units. Theseprocessing units can be physically located within the same device, orprocessor(s) 104 can represent processing functionality of a pluralityof devices or software functionality operating alone, or in concert.

The processor(s) 104 can be configured to execute machine-readableinstructions 106 or machine learning modules via software, hardware,firmware, some combination of software, hardware, and/or firmware,and/or other mechanisms for configuring processing capabilities onprocessor(s) 104. As used herein, the term “machine-readableinstructions” can refer to any component or set of components thatperform the functionality attributed to the machine-readableinstructions component 106. This can include one or more physicalprocessor(s) 104 during execution of processor-readable instructions,the processor-readable instructions, circuitry, hardware, storage media,or any other components.

The server(s) 102 can be configured with machine-readable instructionshaving one or more functional modules. The machine-readable instructions106 can be implemented on one or more servers 102, having one or moreprocessor(s) 104, with access to memory 130. The machine-readableinstructions 106 can be a single networked node, or a machine cluster,which can include a distributed architecture of a plurality of networkednodes. The machine-readable instructions 106 can include control logicfor implementing various functionality, as described in more detailbelow. The machine-readable instructions 106 can include certainfunctionality associated with the system 100. Additionally, themachine-readable instructions 106 can include a smart contract ormulti-signature contract that can process, read, and write data to thedatabase, distributed ledger, or blockchain.

FIG. 4 illustrate a schematic view of a railroad inspection system 400,in accordance with one or more exemplary embodiments of the presentdisclosure. The system 400 can include a data collection system 402,data parsing system 404, and inspection rating system 406. Althoughcertain exemplary embodiments may be directed to rail assets, the system400 can be used to plan maintenance for a type of railroad asset,including rails, ballasts, panels, ties, turnouts, facilities, or anysuitable asset.

In one exemplary embodiment, the data collection system 402 can includea CSP data collection module 308, MGT data collection module 310,precipitation data collection module 312, geometry car tag collectionmodule 314, BFI data collection module 316, slow order data collectionmodule 318, and manual defect collection module 320. The CSP datacollection module 308, MGT data collection module 310, precipitationdata collection module 312, geometry car tag collection module 314, BFIdata collection module 316, slow order data collection module 318,manual defect collection module 320 can implement one or more algorithmsto connect to a network to communicate with a client, ultimately tofacilitate railroad asset inspection and communicate data related to aninspection frequency for a segment of track. The algorithms and theirassociated thresholds and/or signatures can be programmable to suit aparticular railroad asset, application, function, facility, or otherrequirement. The data collection system 402 can be configured to sendand receive messages related to an inspection or other suitableactivity, to and from the client or server. In another exemplaryembodiment, the data collection system 402 can generate one or moreelements for display on the user device. The elements can provideadditional information to the user related to an inspection. Forexample, alerts can be generated by the data collection system 402 anddisplayed on the client to indicate data collection, data collectioncompletion, data submission, data request, or other suitableinformation. Additionally, system symbols can be displayed on the clientto indicate data collection status.

The CSP data collection module 308, in an embodiment, can collect theCSPs for the segments of track. For example, the CSP data collectionmodule 308 can identify CSPs for the segments of track based on aservice plan queue and store the CSPs in a tabular form in an externaldatabase. The external database can include an external memory, virtualmachine implementation, or cloud environment, or any other environmentsuitable to queue the CSPs. In an example the location of the CSPs canbe based on milepost measurements of the segment of track. In anotherexample, the CSP data collection module 308 can remove any redundantinformation from the CSP data. For example, the data can include firstCSP information across a first segment of track at a first time andsecond CSP information across a second segment of track at a secondtime. When the second segment of track is entirely included within thefirst segment of track, the CSP data collection module 308 can removethe second CSP information as redundant. In another example, when thesecond CSP information includes a segment of track entirely includedwithin the first CSP information, the CSP data collection module 308 canskip the redundant information.

In another example, the CSP data collection module 308 can segment theCSP data into CSP segments and non-CSP segments. In this way, the CSPdata collection module 308 can include CSP information on the entiresegment of track, rather than part of the segment of track. For example,the CSP segments can include the CSP information corresponding to thesegment of track, while the non-CSP segments can include a placeholdervalue. In an example, the non-CSP segments can correspond to lengths ofthe track without any CSP information. The CSP data collection module308 can determine the lengths of the track without CSP information byidentifying the positions of the segments of the track with CSPinformation and calculating a difference between the positions of thesegments of the track. The CSP data collection module 308 can assign thedifference between the positions as the non-CSP segments. In anotherexample, the CSP data collection module 308 can assign the CSP data toeach of a set of parallel tracks. For example, when the CSP dataincludes a single track segment for a parallel track segment, the CSPdata collection module 308 assigns the CSP data to both of the paralleltracks. The CSP data collection module 308 can update the CSP data forboth of the parallel tracks.

The MGT data collection module 310, in an embodiment, can collect theMGT data from a database. For example, the MGT data can include the MGTacross a particular segment of track for a particular year. The MGTacross the particular segment of track can include measurements from anexternal sensor active along the segment of track to measure the MGT aslocomotives cross the location where the external sensor is measuring.In another example, the external sensor can update the database at afrequency usable for the particular application (i.e., in real-time,daily, weekly, etc.). In an example, the MGT data collection module 310can access the database and search the database according to railwayinformation. In an example, the railway information can include aparticular year, a line segment of the track, a track type, a tracknumber, and a center point of a mile post range. For example, an examplesearch query can include the following input.

LS-05, Track Type-M, Track nbr-1, Begin MP-4.6463, End MP-4.7580. Where“LS-05” indicates a line segment identifier of 05, “Track Type-M” is amain track, “Track nbr-1” is an identifier for the particular track,“Begin MP-4.6463” identifies the mile post starting point at 4.6463, and“End MP-4.7580” identifies the mile post ending point at 4.7580.

In another example, the MGT data collection module 310 can collect theMGT data from the database on a monthly basis to calculate a latest MGTand a defect MGT. For example, the MGT data collection module 310 cancalculate the latest MGT based on a daily MGT. The daily MGT cancorrespond to an calculated estimate on the daily tonnage across thesegment of track based on the MGT data measured from the external sensorover a period of time. For example, the MGT data collection module 310can query the database to collect the MGT data across the LS for onemonth (30 days). The MGT data collection module 310 can calculate thedaily MGT by dividing the MGT data for the one month by the number ofdays. For example, the MGT data collection module can divide the MGTdata by 30 days to calculate the daily MGT.

In an example, the latest MGT is calculated by multiplying the daily MGTby the difference between the current date and the last test date, asshown below.

Daily MGT calculated*no. of day (Current date−latest test date).

In another example, the defect MGT is calculated based on the daily MGTmultiplied by the difference between the current date and the date ofthe first occurrence of a tag type. For example, the defect MGT can becalculated for a red tag as shown below.

Daily MGT calculated*no. of day (Current date−first occur red tag testdate).

The precipitation data collection module 312, in an embodiment, cancollect precipitation data from a database based on a location of thesegment of track. For example, the database can include precipitationdata from a particular timeframe. In an example, when the database lacksany precipitation data for the timeframe, the precipitation datacollection module 312 can estimate the precipitation data based onprecipitation data from another timeframe. For example, theprecipitation data collection module 312 can compare a length of thetimeframe with the other timeframe and calculate the averageprecipitation based on the precipitation data available from the othertimeframe. In another example, the precipitation data collection module312 can multiply the average precipitation from the other timeframe by auser defined precipitation multiplier to overestimate the amount ofrainfall. In another example, the precipitation data collection module312 can access a public weather data repository to collect public accessweather data for the precipitation data.

The geometry car tag collection module 314, in an embodiment, canidentify defects across the segment of track. For example, a geometrycar will travel across the segment of track to identify any defects andtag the defects accordingly. The tags can include physical tags toidentify a defect, or virtual tags in the form of a location identifier,an open or closed value, and a severity tier. The number of defects caninclude at least one level of severity. In an example, the at least onelevel of severity can correspond to at least one tag type. For example,the at least one tag type can include a yellow tag as the lowest levelof severity, the orang tag is the middle level of severity, and the redtag is the highest level of severity. In another example, the geometrycar tag collection module 314 can determine the number of defectscorresponding to at least one tag type. In an example, the geometry tagcollection module 314 can search a database for a particular tag type toidentify corresponding segments of track. The geometry car tagcollection module 314 can identify the defect count for both CSPsegments and non-CSP segments.

The BFI data collection module 316, in an embodiment, can collect BFIdata corresponding to the segment of track from a database. For example,the BFI data can include an average BFI value of the segment of trackwith corresponding ballast test dates. The average BFI value of thesegment of track can include measurement from an external sensormeasuring the fouling of the ballast. In another example, the externalsensor can update the database at a frequency usable for the particularapplication (i.e., in real-time, daily, weekly, etc.). In an example,the BFI data collection module 316 can access the database and searchthe database according to railway information. For example, the railwayinformation can include a particular year, a line segment of the track,a track type, a track number, and a center point of a mile post range.The database can include the BFI data for the previous year whensearched by the BFI data collection module 316. In an example, when thedatabase lacks the BFI data for the line segment for a particular date,the BFI data collection module 316 can compare previous BFI data andassign the maximum of the previous BFI data for the BFI data of theparticular date.

The slow order data collection module 318, in an embodiment, can collectslow order data from a database. For example, the slow order data caninclude slow orders across the segment of track for a particular year.In an example, the slow order indicates when locomotives must travel ata reduced speed across the segment of track. The database can includethe slow order data across the segment of track including a collectionof notices for the segment of track indicating when a slow order wasactive and a corresponding date. In an example, when the slow order isactive across the segment, the speed requirements can vary depending ona class drop requirement and a rating of the slow order. For example,when the locomotive is a Class-4 in a CSP segment and the slow orderrequires a class drop of 2 and a rating of 3, the slow order speed forthe locomotive is equivalent to a Class-2 locomotive. In an example,when the locomotive is a Class-3 in a CSP segment and the slow orderrequires a class drop of 2 and a rating of 3, the slow order speed forthe locomotive is equivalent to a Class-3 locomotive because theoriginal class of the locomotive meets the rating. In another example,when the locomotive is a Class-5 in a non-CSP segment and the slow orderrequires a class drop of 3 and a rating of 4, the slow order speed forthe locomotive is equivalent to a Class-2 locomotive. In an example,when the locomotive is a Class-4 in a non-CSP segment and the slow orderrequires a class drop of 3 and a rating of 4, the slow order speed forthe locomotive is unchanged because the original class of the locomotiveis equivalent to the rating.

The manual defect collection module 320, in an embodiment, can collectmanual defect data from a database. For example, the manual defectcollection module 320 can identify the total number of manual defectsfrom the database across the segment of track. In an example, thedatabase can update the manual defect data based on inputs from a clientor external sensor from a manual or automatic inspection. For example,when an inspector identifies a defect not previously detected, theinspector will collect the manual defect data using the client or theexternal sensor and update the manual defect data in the database toreflect the new defect. In an example, the manual defect collectionmodule 320 can query the database to identify the total number of manualdefects. In another example, the manual defect data can include railwaydefect information. The railway defect information can include defectsacross turnouts, railway joints, among other railway defects.

In another example, the manual defect collection module 320 can collectthe manual defect data from the database based on line segment, tracktype, track number, and mile post range. The database can return anidentifier for the query. In an example, the manual defect collectionmodule 320 can query a manual defect database using the identifier,returning the number of manual defects for the segment of track.

In one exemplary embodiment, the data parsing system 404 can include amessage identification module 322, log collection module 324, andinformation parsing module 326. The message identification module 322,log collection module 324, and information parsing module 326 canimplement one or more algorithms to access the data collected from thedata collection system 402, to identify and categorize the data prior toanalysis of the inspection frequency for the segment of track. Thealgorithms and their associated thresholds and/or signatures can beprogrammable to suit a particular railroad asset, application, function,facility, or other requirement. The data parsing system 404 can beconfigured to send and receive messages related to data collection andcategorization or other suitable activity, to and from the client orserver. In another exemplary embodiment, the data parsing system 404 cangenerate one or more elements for display on the user device. Theelements can provide additional information to the user related to datastorage. For example, alerts can be generated by the data parsing system404 and displayed on the client to indicate initialization of datacollection and storage, data categorization errors or completion, orother suitable information. Additionally, system symbols can bedisplayed on the client to indicate status of the data storage.

The message identification module 322, in an embodiment, can classifythe incoming messages and any notifications from a database. Forexample, the message identification module 322 can receive informationabout the inspection frequency analysis factors from the incomingmessages and the notifications. In an example, the messageidentification module 322 can classify the incoming messagescorresponding to the inspection frequency analysis factors.

The log collection module 324, in an embodiment, can receive queryresponses from the database. For example, the log collection module 324can collect the query responses from the database based on a model datafetch date. In an example, the model data fetch date can include atleast the last week of data generation for the segment of track. Inanother example, the model data fetch date can include a user definedinput for log collection. For example, the log collection module 324 canreceive the query responses for a timeframe entered by a user. The logcollection module 324 can organize data corresponding to the queryresponses based on the classification from the message identificationmodule 322.

The information parsing module 326, in an embodiment, can transform theincoming data, messages, and notification for relevant information tothe inspection frequency analysis factors. For example, the incomingmessages can include information such as CSP data, MGT data,precipitation data, geometry tag data, BFI data, slow order data, andmanual defect data. In an example, the information parsing module 326can establish a connection to the database. For example, the informationparsing module 326 can receive the incoming message from the databaseand transmit the relevant information to the database. The informationtransmitted to the database can include an indicator of an inspectionfrequency analysis is not complete, in progress, or complete.

In one exemplary embodiment, the inspection rating system 406 caninclude an accumulation module 328 and analysis module 330. Theaccumulation module 328 and analysis module 330 can implement one ormore algorithms to connect to a network to communicate with anothersystem of the railroad inspection system 400, ultimately to analyze andassign a score to the data to determine an inspection frequency. Thealgorithms and their associated thresholds and/or signatures can beprogrammable to suit a particular railroad asset, application, function,facility, or other requirement. The network management system 202 can beconfigured to send and receive messages related to an inspection orother suitable activity, to and from the client or server. In anotherexemplary embodiment, the inspection rating system 406 can generate oneor more elements for display on the user device. The elements canprovide additional information to the user related to the inspectionfrequency. For example, alerts can be generated by the inspection ratingsystem 406 and displayed on the client to indicate current and futureinspection frequency, factors used to determine inspection frequency,changes in the inspection frequency, or other suitable information.Additionally, system symbols can be displayed on the client to indicateuser defined variables for assigning the score the segment of track.

The rating module 328, in an embodiment, can transform the data intoratings for each inspection frequency analysis factors. For example, theinspection frequency analysis factors can include the CSP data, the MGTdata, the precipitation data, the geometry tag data, the BFI data, theslow order data, and the manual defects data. The rating module 328 cantransform the ratings corresponding to values of each of the inspectionfrequency analysis factors. For example, the rating module 328 canassign values between 0-5 depending on a severity of the inspectionfrequency analysis factor. In an example, the coded score disclosed inFIG. 1 applies to the values for the inspection frequency analysisfactors.

The accumulation module 330, in an embodiment, can calculate a totalscore for the ratings. For example, the accumulation module 330 can addeach of the ratings together to generate a value for the total score. Inan example, the accumulation module 330 can determine when the segmentof track includes at least one rail joint. When the segment of trackincludes rail joint, the accumulation module 330 multiples the totalscore by a user defined multiplier. For example, the user definedmultiplier is 1.5. In an example, the total score can include a maximumvalue of 48.5 based on the inspection frequency analysis factors.

The analysis module 332, in an embodiment, can determine an inspectionfrequency based on an inspection frequency classification model. Forexample, the inspection frequency classification model can classify theinspection frequency based on the total score for the ratings. In anexample, the analysis module 332 can identify whether the total score iscategorized for a low inspection frequency, a medium inspectionfrequency, or a high inspection frequency. In an example, when the totalscore is below 6, the analysis module 332 assigns the low inspectionfrequency to the segment of track. In another example, when the totalscore is between 6 and 12, the analysis module 332 assigns the mediuminspection frequency to the segment of track. In another example, whenthe total score is greater than 12, the analysis module 332 assigns thehigh inspection frequency to the segment of track. In an example, theanalysis module 332 can determine when the aggregated daily MGT from alatest geometry car test date. When the aggregated daily MGT from thelatest geometry car test date is greater than or equal to 12, theanalysis module 332 increases the inspection frequency. In an example,when the inspection frequency is one time per week, the analysis module332 increases the inspection frequency to three times per week. In anexample, when the inspection frequency is two times per month, theanalysis module 332 increases the inspection frequency to one time perweek.

FIG. 5 illustrates railroad inspection control logic 500, in accordancewith one or more exemplary embodiments of the present disclosure. Therailroad inspection control logic 500 can be implemented as an algorithmon a server 302, a machine learning module, a client (e.g., clients 350,352, 354), a database, or other suitable system. Additionally, therailroad inspection control logic 500 can implement or incorporate oneor more features of the railroad inspection system 400, including thedata collection system 402, data parsing system 404, and inspectionrating system 406. The railroad inspection control logic 500 can beachieved with software, hardware, firmware, an API, a networkconnection, a network transfer protocol, HTML, DHTML, JavaScript, Dojo,Ruby, Rails, other suitable applications, or a suitable combinationsthereof.

The railroad inspection control logic 500 can leverage the ability of acomputer platform to spawn multiple processes and threads by processingdata simultaneously. In an embodiment, the railroad inspection controllogic 500 can implement a memory for storing the acquired data relatingto the railroad safety maintenance and an inspection frequencyclassification model to transform the acquired data from a raw form to afinal inspection frequency value. In another embodiment, the railroadinspection control logic 500 can instantiate instructions for aprocessor that is configured to transform the acquired data relating tothe railroad safety maintenance from the raw form to the finalinspection frequency value. The speed and efficiency of the railroadinspection control logic 500 can be greatly improved by instantiatingmore than one process to implement parallel railroad asset inspections.However, one skilled in the art of programming will appreciate that useof a single processing thread may also be utilized and is within thescope of the present disclosure. The railroad inspection control logic500 of the present embodiment begins at step 502.

At step 502, in an embodiment, the control logic 500 can receive thedata relating to railroad safety maintenance for at least one segment ofrailroad track. For example, the control logic 500 can receive data fromthe data collection system 402 shown in FIG. 4 . In an example, thecontrol logic 500 can acquire the data relating to railroad safetymaintenance from a database including CSP data, MGT data, precipitationdata, geometry car tag data, BFI data, slow order data, and manualdefect data, or any combination of the foregoing data. In an example,the data can be structured or unstructured. For example, the controllogic 500 can execute database access instructions corresponding to thestructured data with particular variables in the instructions to receiveaccording to the structure of the data. In an example, the control logic500 can execute SQL database instructions indicating the segment oftrack and data factors to receive. The control logic 500 then proceedsto step 504.

At step 504, in an embodiment, the control logic 500 can segment thedata into a plurality of categories. For example, the data can originatefrom a plurality of sources. In an example, the data can include uniqueidentifiers for each of the sources. The control logic 500 can segmentthe data by comparing the unique identifiers of the sources withcategory labels matching to the unique identifiers. For example, thecontrol logic 500 can include unique numeric identifiers associated witheach of the railroad safety maintenance factors. In an example, thecontrol logic 500 can organize the data based on the categories such asCSP values, MGT values, precipitation values, geometry car tag values,BFI values, slow order values, and manual defect values. In an example,the geometry car tag values can include an open tag or a closed tag. Theopen tag can indicate the segment of track includes an active defect.The closed tag can indicate the segment of track includes a fixeddefect. For example, the open tag can convert to the closed tag when thedefect corresponding to the open tag is fixed. In another example, theopen tag and the closed tag can include various levels of tag types. Forexample, the tag types can include a first tag type, a second tag type,and a third tag type. The first tag type can represent a minor issuewith the segment of track. The second tag type can represent a moderateissue with the segment of track. The third tag type can represent acritical issue with the segment of track. In another example, each ofthe categories corresponds with one of the unique identifiers. Inanother example, the control logic 500 can identify the category thedata belongs based on comparing the data to expected values andassigning the data to a category matching the expected values. Thecontrol logic 500 can assign the data to the category based onpredetermined conditions according to units associated to the data. Thecontrol logic 500 then proceeds to step 506.

At step 506, in an embodiment, the control logic 500 can assign railroadsafety values corresponding to the categories based on a user definedhazard model. For example, each of the categories can correspond to anindependent railroad safety value of the railroad safety values. In anexample, one of the railroad safety values corresponding to a first CSPvalue for a first segment of track, which can be different from anotherof the railroad safety values corresponding to a second CSP value for asecond segment of track. For example, the first segment of track caninclude various known non-compliant characteristics leading to a maximumCSP value, while the second segment of track can include totallycompliant characteristics leading to a minimum CSP value. The controllogic 500 can assign the railroad safety values as described in FIG. 1 .The control logic 500 then proceeds to step 510.

At step 508, in an embodiment, the control logic 500 can aggregate therailroad safety values to generate a railroad safety score. For example,the control logic 500 can add together the railroad safety values togenerate the railroad safety score. The control logic 500 then proceedsto step 510.

At step 510, in an embodiment, the control logic 500 can determinewhether MGT data includes information identifying rail joints. Forexample, the MGT data can include a variable indicating whether thesegment of track includes at least one rail joint. The variable can bebinary, where a “0” can represent the segment of track lacks any railjoints and a “1” can represent the segment of track includes railjoints. If the control logic 500 determines the MGT data includesinformation identifying rail joints, the control logic 500 then proceedsto step 512. If the control logic 500 determines the MGT data includesinformation without rail joints, the control logic 500 then proceeds tostep 514.

At step 512, in an embodiment, the control logic 500 can multiply therailroad safety score by a user defined multiplier. For example, theuser defined multiplier can be 1.5. The user defined multiplier can bemodified based on input from the user. The control logic 500 can receiveinput from the user to update the multiplier to a new value. The controllogic 500 then proceeds to step 514.

At step 514, in an embodiment, the control logic 500 can determinewhether the inspection frequency meets a minimum inspection frequencythreshold. For example, the control logic 500 can measure the MGT acrossthe segment of track over a period of time and compare the MGT to theminimum inspection frequency threshold. For example, the minimuminspection frequency threshold can include a predefined MGT representinga limit for the segment of track. In an example, when the MGT across thesegment of track is greater than the predefined MGT, the control logic500 can elevate the inspection frequency. In another example, the periodof time can include a timeframe between inspections according to theinspection frequency. In an example, the inspection frequency cancorrespond to a tiered structure. For example, the minimum inspectionfrequency threshold can be a first tier, a second tier, or a third tier.In an example, the first tier is a lowest frequency for inspectionpurposes, the second tier is a medium frequency for inspection, and thethird tier is a highest frequency for inspection. The first tier caninclude a frequency of two inspections per month. The second tier caninclude a frequency of one inspection per week. The third tier caninclude a frequency of three inspections per week. The control logic 500can compare the inspection frequency with the known inspectionfrequencies to determine whether the inspection frequency meets theminimum inspection frequency threshold. For example, the minimuminspection frequency threshold can be the first tier. If the inspectionfrequency meets the minimum inspection frequency threshold, the controllogic 500 then proceeds to step 518. If the inspection frequency doesnot meet the minimum inspection frequency threshold, the control logic500 then proceeds to step 516.

At step 516, in an embodiment, the control logic 500 can determinewhether to increase an inspection frequency of the segment of track. Forexample, when the inspection frequency is equal to or greater than theminimum inspection frequency threshold, the control logic 500 canelevate the inspection frequency for the segment of track to the nexttier of the tiered structure. In an example, if the inspection frequencyis two inspections per month, the control logic 500 can elevate theinspection frequency to one inspection per week. In another example, ifthe inspection frequency is one inspection per week, the control logic500 can elevate the inspection frequency to three inspections per week.The control logic 500 then proceeds to step 518.

At step 518, in an embodiment, the control logic 500 can generate afinal inspection frequency for the at least one segment of track basedon the railroad safety score. The control logic 500 can generate thefinal inspection frequency with respect to the information correspondingto whether the segment of track includes rail joints and the MGT acrossthe segment of track over the period of time. The control logic 500 thenterminates or awaits a new inspection creation request and can repeatthe aforementioned steps.

FIGS. 6A-C and FIG. 7 provide an exemplary embodiment of identifyingrailway asset defects across four segments of track (i.e., tracksegments 602-608) and a corresponding process. The segments of track canextend indefinitely on either side, only a section of track ispresented. These examples are intended to facilitate an understanding ofthe ways in which the disclosure can be implemented and practiced andshould not limit the disclosure to only these examples.

FIGS. 6A-6C illustrate exemplary diagrams for a railroad inspectionsystem 600, in accordance with one or more exemplary embodiments of thepresent disclosure. The diagrams illustrate the railroad inspectionsystem 600 with marked railroad asset defects along various segments oftrack based on geometry car testing and defect tagging. The diagram 600can include track segments 602-608 and at least one marked railroadasset defect 610.

The track segments 602-608 can correspond to mile post ranges of asegment of a track. For example, track segment 602 can correspond to amile post range between MP 100 and MP 200, where MP 100 is on theleft-most side of the track segment 602 and MP 200 is on the right-mostside of the track segment 602. The track segment 606 can correspond to amile post range between MP 200 and MP 300, where MP 200 is on theleft-most side of the track segment 606 and MP 300 is on the right-mostside of the track segment 606. The track segment 604 can correspond to amile post range parallel to the track segment 602 between MP 90 and MP190, where MP 90 is on the left-most side of the track segment 604 andMP 190 is on the right-most side of the track segment 604. The tracksegment 608 can correspond to a mile post range parallel to the tracksegment 606 between MP 190 and MP 290, where MP 190 is on the left-mostside of the track segment 608 and MP 290 is on the right-most side ofthe track segment 608.

The system 600 can illustrate the path along the track segments 602-608a locomotive can travel across based on a geometry of the track segments602-608. For example, the track segment 602 includes a rail joint fromthe track segment 602 to the track segment 604, so a locomotive cantravel from the track segment 602 to the track segment 604. In anexample, a geometry car will travel along the track segments 602-608 inevery applicable geometry of the track segments 602-608 to identify theat least one marked railroad asset defect 610. In another example, thetrack segments 602-608 can include incremental measurements fornormalized analysis. For example, the track segments 602-608 can allinclude the incremental measurements at equal lengths according to milepost measurements. For example, each of the track segments 602-608 caninclude the incremental measurement of 100 MP. In this way, the tracksegments 602-608 can all be compared to one another for accuratecomparisons of relative track segment status. The relative track segmentstatus can correspond to the at least one marked railroad asset defect610 among other railway factors to determine an inspection frequency.

The at least one marked railroad asset defect 610 can represent ahazardous point along the track segments 602-608. For example, tracksegment 602 can include three defects as shown in FIG. 6 . In anexample, track segment 604 can include one defect as shown in FIG. 6 .In another example, track segment 606 can include five defects as shownin FIG. 6 . In another example, track segment 608 can include ninedefects as shown in FIG. 6 . In an example, the at least one markedrailroad asset defect 610 can correspond to geometry tag data includingthe coded score according to defect severity (i.e., the geometry tagdata 108 of FIG. 1 ). In an example, the defects can include a firsttag, type, a second tag type, and a third tag type. In another example,the defects can include an open tag and a closed tag.

In an embodiment, referring to FIG. 6A, the system 600 can be used toanalyze the inspection frequency along each of the track segment602-608. For example, based strictly on the number of defects, theinspection frequency for track segment 604 can be lower frequency thanthe other track segments because track segment 604 has the fewestdefects. In another example, the track segment 604 can include otherfactors impacting the inspection frequency which could increase theinspection frequency upon analysis. For example, the track segment 604can include a highest degree of MGT across the track segment 604, whichwould elevate a total rating for the track segment 604. The total ratingof the track segment 604 corresponds with the inspection frequency. Inanother example, the track segment 604 can include rail joints, whichwould lead to the total rating being multiplied by a user definedmultiplier upon analysis. The additional factors to the number ofdefects can impact the total rating, which can lead to an increasedinspection frequency.

In another example, referring to FIG. 6B, based completely on the numberof defects, the inspection frequency for track segment 608 can be higherfrequency than the other track segments because track segment 608 hasthe greatest number of defects. The inspection frequency of the tracksegment 608 can correspond to the types of tags corresponding to thedefects. For example, the track segment 608 can include nine defects,where eight defects are closed tags and one defect is an open tag thatis the first tag type. Additionally, the track segment 604 can includeone defect, where the defect is an open tag that is the third tag type.In this case, based strictly on the number and type of defects, thetrack segment 608 can include a lower inspection frequency than thetrack segment 604.

In another example, referring to FIG. 6C, the inspection frequency forthe track segments 602-608 can be based on a plurality of categories ofrailroad safety data. For example, in addition to the at least onemarked railroad asset defect 610, the categories can include CSP values,MGT values, precipitation values, geometry car tag values, BFI values,slow order values, and manual defect values. The inspection frequency ofeach of the track segments 602-608 can correspond to an analysis of theabove categories of railroad safety data.

Referring to FIG. 7 as a process for railroad inspection 700, inaccordance with one or more exemplary embodiments of the presentdisclosure. The process for railroad inspection 700 of the presentembodiment begins at step 702. At step 702, in an embodiment, theprocess 700 can instruct a system to identify track segments forpredetermined distances for inspection purposes. The process 700 thenproceeds to step 704. At step 704, in an embodiment, the process 700 caninstruct the system to establish subdivisions of the track segments. Theprocess 700 then proceeds to step 706. At step 706, in an embodiment,the process 700 can instruct the system to receive data relating torailroad safety maintenance of the track segments. The process 700 thenproceeds to step 708. At step 708, in an embodiment, the process 700 caninstruct the system to identify defects and defect locationscorresponding to the track segments based on the data. The process 700then proceeds to step 710. At step 710, in an embodiment, the process700 can instruct the system to aggregate the railroad safety values togenerate a railroad safety score. The process 700 then proceeds to step712. At step 712, in an embodiment, the process 700 can instruct thesystem to generate a final inspection frequency for the at least onesegment of railroad track based on the railroad safety score.

The present disclosure achieves at least the following advantages:

1. Generating a comprehensive, data-driven inspection frequency modelbased on measured physical factors relevant for determining an optimumvisual inspection frequency for a segment of track.

2. Transforming data corresponding to physical factors across a segmentof track to identify a visual inspection frequency.

3. Complying with relevant federal regulations corresponding toinspection of railroad.

4. Normalizing data across various segments of track to accuratelydetermine an inspection frequency for the segments of track.

5. Assigning safety ratings to segments of track based on measured datavalues for physical factors associated with railway travel.

6. Enhancing safety for passengers and cargo along railways bydetermining optimum inspection frequencies for the segments of track.

Persons skilled in the art will readily understand that advantages andobjectives described above would not be possible without the particularcombination of computer hardware and other structural components andmechanisms assembled in this inventive system and described herein.Additionally, the algorithms, methods, and processes disclosed hereinimprove and transform any general-purpose computer or processordisclosed in this specification and drawings into a special purposecomputer programmed to perform the disclosed algorithms, methods, andprocesses to achieve the aforementioned functionality, advantages, andobjectives. It will be further understood that a variety of programmingtools, known to persons skilled in the art, are available for generatingand implementing the features and operations described in the foregoing.Moreover, the particular choice of programming tool(s) may be governedby the specific objectives and constraints placed on the implementationselected for realizing the concepts set forth herein and in the appendedclaims.

The description in this patent document should not be read as implyingthat any particular element, step, or function can be an essential orcritical element that must be included in the claim scope. Also, none ofthe claims can be intended to invoke 35 U.S.C. § 112(f) with respect toany of the appended claims or claim elements unless the exact words“means for” or “step for” are explicitly used in the particular claim,followed by a participle phrase identifying a function. Use of termssuch as (but not limited to) “mechanism,” “module,” “device,” “unit,”“component,” “element,” “member,” “apparatus,” “machine,” “system,”“processor,” “processing device,” or “controller” within a claim can beunderstood and intended to refer to structures known to those skilled inthe relevant art, as further modified or enhanced by the features of theclaims themselves, and can be not intended to invoke 35 U.S.C. § 112(f).Even under the broadest reasonable interpretation, in light of thisparagraph of this specification, the claims are not intended to invoke35 U.S.C. § 112(f) absent the specific language described above.

The disclosure may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. For example, eachof the new structures described herein, may be modified to suitparticular local variations or requirements while retaining their basicconfigurations or structural relationships with each other or whileperforming the same or similar functions described herein. The presentembodiments are therefore to be considered in all respects asillustrative and not restrictive. Accordingly, the scope of theinventions can be established by the appended claims rather than by theforegoing description. All changes which come within the meaning andrange of equivalency of the claims are therefore intended to be embracedtherein. Further, the individual elements of the claims are notwell-understood, routine, or conventional. Instead, the claims aredirected to the unconventional inventive concept described in thespecification.

What is claimed is:
 1. A system for minimizing safety hazards along a railroad track by optimizing visual railroad inspection frequency based on acquired data relating to railroad safety maintenance, comprising: a memory for storing the acquired data relating to the railroad safety maintenance and an inspection frequency classification model to transform the acquired data from a raw form to a final inspection frequency value; and a processor that is configured to transform the acquired data relating to the railroad safety maintenance from the raw form to the final inspection frequency value, by performing the steps of: receiving the acquired data relating to railroad safety maintenance for at least one segment of railroad track; assigning a railroad safety value for each of a plurality of categories of the acquired data based on a user defined hazard model; aggregating the railroad safety values to generate a railroad safety score; when a metric gross ton (MGT) calculation includes a rail joint factor, multiplying the railroad safety score by a user defined multiplier; and generating the final inspection frequency value for the at least one segment of railroad track based on the railroad safety score; generating an alert indicating the final inspection frequency to perform inspections.
 2. The system of claim 1, wherein the processor is further configured to perform the step of segmenting the acquired data into the plurality of categories.
 3. The system of claim 2, wherein the plurality of categories includes comprehensive service plan (CSP), MGT, precipitation, geometry car tag, ballast fouling index (BFI), slow order, and manual defects.
 4. The system of claim 3, wherein the geometry car tag includes an open tag or a closed tag.
 5. The system of claim 4, wherein the open tag and the closed tag each correspond to a first tag type, a second tag type, or a third tag type.
 6. The system of claim 1, wherein the processor is further configured to perform the step of determining whether to increase an inspection frequency of the segment of railroad track when the segment of railroad track corresponds to a minimum inspection criteria threshold.
 7. The system of claim 6, wherein the minimum inspection criteria threshold includes a measurement of at least 12 MGT across the segment of railroad track since a previous test.
 8. The system of claim 6, wherein the inspection frequency increases to a next available railroad manual inspection frequency.
 9. The system of claim 1, wherein the final inspection frequency value is based on user defined inspection thresholds including a first railroad manual inspection frequency, a second railroad manual inspection frequency, and a third railroad manual inspection frequency.
 10. The system of claim 9, wherein the first railroad manual inspection frequency is two inspections per month, the second railroad manual inspection frequency is one inspection per week, and the third railroad manual inspection frequency is three inspections per week.
 11. A method for minimizing safety hazards along a railroad track by optimizing visual railroad inspection frequency based on acquired data relating to railroad safety maintenance, comprising: receiving the acquired data relating to railroad safety maintenance for at least one segment of railroad track; assigning a railroad safety value for each of a plurality of categories based on a user defined hazard model; aggregating the railroad safety values to generate a railroad safety score; when a metric gross ton (MGT) calculation includes a rail joint factor, multiplying the railroad safety score by a user defined multiplier; and generating the final inspection frequency value for the at least one segment of railroad track based on the railroad safety score; generating an alert indicating the final inspection frequency to perform inspections.
 12. The method of claim 11, wherein the processor is further configured to perform the step of segmenting the acquired data into the plurality of categories.
 13. The method of claim 12, wherein the plurality of categories includes comprehensive service plan (CSP), MGT, precipitation, geometry car tag, ballast fouling index (BFI), slow order, and manual defects.
 14. The method of claim 13, wherein the geometry car tag includes an open tag or a closed tag.
 15. The method of claim 14, wherein the open tag and the closed tag each correspond to a first tag type, a second tag type, or a third tag type.
 16. The method of claim 11, wherein the processor is further configured to perform the step of determining whether to increase an inspection frequency of the segment of railroad track when the segment of railroad track corresponds to a minimum inspection criteria threshold.
 17. The method of claim 16, wherein the minimum inspection criteria threshold includes a measurement of at least 12 MGT across the segment of railroad track since a previous test.
 18. The method of claim 16, wherein the inspection frequency increases to a next available railroad manual inspection frequency.
 19. The method of claim 11, wherein the final inspection frequency value is based on user defined inspection thresholds including a first railroad manual inspection frequency, a second railroad manual inspection frequency, and a third railroad manual inspection frequency.
 20. The method of claim 19, wherein the first railroad manual inspection frequency is two inspections per month, the second railroad manual inspection frequency is one inspection per week, and the third railroad manual inspection frequency is three inspections per week.
 21. A computer-implemented method for minimizing safety hazards along a railroad track by optimizing visual railroad inspection frequency based on acquired data relating to railroad safety maintenance, the computer-implemented method comprising: receiving the acquired data relating to railroad safety maintenance for at least one segment of railroad track; assigning a railroad safety value for each of a plurality of categories based on a user defined hazard model; aggregating the railroad safety values to generate a railroad safety score; when a metric gross ton (MGT) calculation includes a rail joint factor, multiplying the railroad safety score by a user defined multiplier; and generating the final inspection frequency value for the at least one segment of railroad track based on the railroad safety score; generating an alert indicating the final inspection frequency to perform inspections.
 22. The computer-implemented method of claim 21, wherein the processor is further configured to perform the step of segmenting the acquired data into the plurality of categories.
 23. The computer-implemented method of claim 22, wherein the plurality of categories includes comprehensive service plan (CSP), MGT, precipitation, geometry car tag, ballast fouling index (BFI), slow order, and manual defects.
 24. The computer-implemented method of claim 23, wherein the geometry car tag includes an open tag or a closed tag. 